
In this work, we proposed two GAN-based approaches to address the problem of sketch generation in vector format. Until now, only a handful of approaches based on VAE [1]}, [2]}, [3]}, [4]}, address this problem. We proposed two architectures viz. SkeGAN and VASkeGAN, the former a standalone GAN with policy gradients and adversarial loss, while the latter based on VAE-GAN. SkeGAN generates sketches that are better, both qualitatively and quantitatively, than those generated by VASkeGAN and has a faster convergence than VASkeGAN. SkeGAN generated sketches that are clear, natural and artistic compared to those from the dataset while maintaining a stable training process. Most importantly, both VASkeGAN and SkeGAN overcome the “scribble effect" of [2]}, thus highlighting their usefulness. Future directions include generalizing this work to larger vector-art datasets, including cartoons.
